How can it be shown clearly and transparently that the outcomes of data-driven health and care technology are validated?
The current version of the document Code of conduct for data-driven health and care technology provides more details about all principles, including principle number 7, which I will quote here.

Consider how the introduction of AI will change relationships in
health and care provision, and the implications of these changes for
responsibility and liability. Use current best practice on how to
explain algorithms to those taking actions based on their outputs.
When building an algorithm, be it a stand-alone product or integrated
within a system, show it clearly and be transparent of the learning
methodology (if any) that the algorithm is using. Undertake ethical
examination of data use specific to this use-case. Achieving
transparency of algorithms that have a higher potential for harm or
unintended decision-making, can ensure the rights of the data subject
as set out in the Data Protection Act 2018 are met, to build trust in
users and enable better adoption and uptake.
Work collaboratively with partners, specify the context for the
algorithm, specify potential alternative contexts and be transparent
on whether the model is based on active, supervised or unsupervised
learning. Show in a clear and transparent specification:

the functionality of the algorithm
the strengths and limitations of the algorithm (as far as they are known)
its learning methodology
whether it is ready for deployment or still in training
how the decision has been made on the acceptable use of the algorithm in the context it is being used (for example, is there a
committee, evidence or equivalent that has contributed to this
decision?)
the potential resource implications

This specification and transparency in development will build trust in
incorporating machine-led decision-making into clinical care.